Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 56, Issue 1, Pages 51-58Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/S0169-7439(01)00108-3
Keywords
artificial neural network (ANN); granulation; in-line moisture measurement; near-infrared (NIR) spectroscopy; partial least squares (PLS); process analytical chemistry (PAC)
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The near-infrared set-up based on simultaneous detection of four wavelengths was applied for in-line moisture measurement during fluid bed granulation. In addition to the spectral response, several other process measurements describing the stare of the granulation were evaluated. The near-infrared moisture measurement is disturbed by the variation in physical properties of the sample (eg, temperature, particle size, bulk density). The factors explaining the non-linearity of spectral response during different phases of granulation could he extracted. Combining all this process information improved the prediction capability of the multivariate calibration models tested (partial least squares and artificial neural network (ANN)). The back-propagation neural network approach was found to have most predictive power with the independent test data. (C) 2001 Elsevier Science B.V. All rights reserved.
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